Monday, February 15, 2016

Is a methodology an essential ingredient in a scientific discipline, so essential that it needs to be mentioned in the name? Digital Humanities is a commonly used name for a research activity where computers are used to support endeavours within humanities and social sciences. Similar combined terms are, for example, computational linguistics and bioinformatics. Some disciplines such as mathematics, statistics, computer science and logic in philosophy are already themselves methodologically oriented. Is the use of computers, at some time in the future, so commonplace and obvious in digital humanities that the qualifying part is left away? In which way is the qualification relevant? Considering good research in humanities, is it necessary to make a difference between approaches that make use of computers and those that do not?

As a starting point to the discussion one could state a claim that the objects of study and phenomena considered in humanities and social sciences are even much more complex than the ones of physical sciences and biological sciences. Human thinking, language and culture are dynamical phenomena, subject to continuous change. A theory may become invalid due to itself. Also in physics measurement influence the results but this effect is not as complex and unbredictable as in humanities. Many approaches in ”simpler” sciences are based on the concept of predictability. Scientist look for experimental settings that can be repeated even as a criteria for being scientific. As Von Foester has stated, such an attitude makes most of research irrelevant from the point of view of real world phenomena. A technical term that can be used here is non-stationarity. Unlike in physics, phenomena discussed in humanities are in constant flux, not only superficially but sometimes even concerning the basic framework. Peter Gärdenfors might explain this as introduction of new quality dimensions. In physics, new quality dimensions may be introduced to craft better theories of physics, but in human behaviours or social activities inherently new dimensions may emerge. These are not new explanatory models but new aspects of the phenomenon itself. What this means in practice is that one cannot compare two situations or contexts in a straightforward way. Due to such complexities, it has been necessary to focus on the use of qualitative methods and textually oriented mode of presentation unless taking a risk of reductionistism. For example, it seems that economics has suffered from such a problem by formulating closed-form equations with small numbers of variables, limited feedback methanisms and consideration of adaptive processes involved. Formalisation is not a guarantee of being scientific if the formalism is not on par with the complexity of the phenomenon under considetation.

Digital humanities is an interesting area because it includes a promise of approaching the complex phenomena in humanities in new ways, facilitated by the availability of large data collections and the latest developments in computer science. The use of the word ”digital” may be considered misleading. Having resources in digital form helps in sharing them, to involve a larger number of researchers than before that can reach the texts and data through networks. A more signiticant impact is, however, reachable through "Computational Humanities." This term can be used to characterize the activity in which humanities data is modeled using modern computer science methods such as statistical machine learning. The complexity of topics at hand motivate development of improved and novel methods that enable modeling data that is more complex than anything seen in physical or biological sciences. Future data sets and analysis processes could, for example, include all books and newspapers published and stored during the history of humankind. This would give a chance to study traditional questions in new ways as well as address wholly new perspectives in holistic manner. Methodological themes to address include non-stationarity, multilayered contextuality and multilevel simulation of large communities of cultural adaptive agents. It will be useful to continue the emerging practice and to bring togethe representatives of humanities and social sciences in one hand and data sciences in the other. The people with formal computing skills need to remember not to bring in reductionistic assuptions and the historians, economists, linguists and others may take a role in which they supervise the assumptions taken in the data driven modeling processes and an analytic role in interpreting the models and results. An essential factor that differs from the past is the chance to rely on emergent processes. They give rise to dynamical understanding that is not dependent on the limited capacity of coding knowledge in small pieces as a manual process. Human interpretation and analysis remains important but can be taken to high level of abstraction or to unforseen level of contextual detail. In this way, Digital Humanities can serve the humankind in its most burning challences and questions related, for example, to successful communication, organizing societies in a good way, solving crises in a peaceful manner, addressing climate change and other environmental issues, improving scientific communication to improve its results and their use, and protect and further refine human cultural heritage.